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<div class="title">graph_alexnet.cpp File Reference</div>  </div>
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<div class="textblock"><code>#include &quot;<a class="el" href="_logger_8h_source.xhtml">arm_compute/core/Logger.h</a>&quot;</code><br/>
<code>#include &quot;<a class="el" href="_graph_8h_source.xhtml">arm_compute/graph/Graph.h</a>&quot;</code><br/>
<code>#include &quot;<a class="el" href="_nodes_8h_source.xhtml">arm_compute/graph/Nodes.h</a>&quot;</code><br/>
<code>#include &quot;<a class="el" href="_c_l_scheduler_8h_source.xhtml">arm_compute/runtime/CL/CLScheduler.h</a>&quot;</code><br/>
<code>#include &quot;<a class="el" href="_c_p_p_scheduler_8h_source.xhtml">arm_compute/runtime/CPP/CPPScheduler.h</a>&quot;</code><br/>
<code>#include &quot;<a class="el" href="_scheduler_8h_source.xhtml">arm_compute/runtime/Scheduler.h</a>&quot;</code><br/>
<code>#include &quot;<a class="el" href="_toolchain_support_8h_source.xhtml">support/ToolchainSupport.h</a>&quot;</code><br/>
<code>#include &quot;<a class="el" href="_graph_utils_8h_source.xhtml">utils/GraphUtils.h</a>&quot;</code><br/>
<code>#include &quot;<a class="el" href="utils_2_utils_8h_source.xhtml">utils/Utils.h</a>&quot;</code><br/>
<code>#include &lt;cstdlib&gt;</code><br/>
<code>#include &lt;iostream&gt;</code><br/>
<code>#include &lt;memory&gt;</code><br/>
</div>
<p><a href="graph__alexnet_8cpp_source.xhtml">Go to the source code of this file.</a></p>
<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
Functions</h2></td></tr>
<tr class="memitem:acbea98d13e0adbf27ecc036feeb610f0"><td class="memItemLeft" align="right" valign="top">std::unique_ptr&lt; <a class="el" href="classarm__compute_1_1graph_1_1_i_tensor_accessor.xhtml">ITensorAccessor</a> &gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a> (const std::string &amp;path, const std::string &amp;data_file)</td></tr>
<tr class="memdesc:acbea98d13e0adbf27ecc036feeb610f0"><td class="mdescLeft">&#160;</td><td class="mdescRight">Generates appropriate accessor according to the specified path.  <a href="#acbea98d13e0adbf27ecc036feeb610f0">More...</a><br/></td></tr>
<tr class="separator:acbea98d13e0adbf27ecc036feeb610f0"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:aa2e0960766de068c13ddff2ab22a8c35"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="graph__alexnet_8cpp.xhtml#aa2e0960766de068c13ddff2ab22a8c35">main_graph_alexnet</a> (int argc, const char **argv)</td></tr>
<tr class="memdesc:aa2e0960766de068c13ddff2ab22a8c35"><td class="mdescLeft">&#160;</td><td class="mdescRight">Example demonstrating how to implement AlexNet's network using the Compute Library's graph API.  <a href="#aa2e0960766de068c13ddff2ab22a8c35">More...</a><br/></td></tr>
<tr class="separator:aa2e0960766de068c13ddff2ab22a8c35"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a217dbf8b442f20279ea00b898af96f52"><td class="memItemLeft" align="right" valign="top">int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="graph__alexnet_8cpp.xhtml#a217dbf8b442f20279ea00b898af96f52">main</a> (int argc, const char **argv)</td></tr>
<tr class="memdesc:a217dbf8b442f20279ea00b898af96f52"><td class="mdescLeft">&#160;</td><td class="mdescRight">Main program for AlexNet.  <a href="#a217dbf8b442f20279ea00b898af96f52">More...</a><br/></td></tr>
<tr class="separator:a217dbf8b442f20279ea00b898af96f52"><td class="memSeparator" colspan="2">&#160;</td></tr>
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<h2 class="groupheader">Function Documentation</h2>
<a class="anchor" id="acbea98d13e0adbf27ecc036feeb610f0"></a>
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          <td class="memname">std::unique_ptr&lt;<a class="el" href="classarm__compute_1_1graph_1_1_i_tensor_accessor.xhtml">ITensorAccessor</a>&gt; get_accessor </td>
          <td>(</td>
          <td class="paramtype">const std::string &amp;&#160;</td>
          <td class="paramname"><em>path</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const std::string &amp;&#160;</td>
          <td class="paramname"><em>data_file</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
</div><div class="memdoc">

<p>Generates appropriate accessor according to the specified path. </p>
<dl class="section note"><dt>Note</dt><dd>If path is empty will generate a DummyAccessor else will generate a NumPyBinLoader</dd></dl>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">path</td><td>Path to the data files </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">data_file</td><td>Relative path to the data files from path</td></tr>
  </table>
  </dd>
</dl>
<dl class="section return"><dt>Returns</dt><dd>An appropriate tensor accessor </dd></dl>

<p>Definition at line <a class="el" href="graph__alexnet_8cpp_source.xhtml#l00054">54</a> of file <a class="el" href="graph__alexnet_8cpp_source.xhtml">graph_alexnet.cpp</a>.</p>

<p>Referenced by <a class="el" href="graph__alexnet_8cpp_source.xhtml#l00071">main_graph_alexnet()</a>.</p>
<div class="fragment"><div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;{</div>
<div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;    <span class="keywordflow">if</span>(path.empty())</div>
<div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;    {</div>
<div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;        <span class="keywordflow">return</span> arm_compute::support::cpp14::make_unique&lt;DummyAccessor&gt;();</div>
<div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;    }</div>
<div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;    <span class="keywordflow">else</span></div>
<div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;    {</div>
<div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;        <span class="keywordflow">return</span> arm_compute::support::cpp14::make_unique&lt;NumPyBinLoader&gt;(path + data_file);</div>
<div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;    }</div>
<div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;}</div>
</div><!-- fragment -->
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<a class="anchor" id="a217dbf8b442f20279ea00b898af96f52"></a>
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          <td class="memname">int main </td>
          <td>(</td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>argc</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const char **&#160;</td>
          <td class="paramname"><em>argv</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
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<p>Main program for AlexNet. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">argc</td><td>Number of arguments </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">argv</td><td>Arguments ( [optional] Path to the weights folder, [optional] batches ) </td></tr>
  </table>
  </dd>
</dl>

<p>Definition at line <a class="el" href="graph__alexnet_8cpp_source.xhtml#l00181">181</a> of file <a class="el" href="graph__alexnet_8cpp_source.xhtml">graph_alexnet.cpp</a>.</p>

<p>References <a class="el" href="graph__alexnet_8cpp_source.xhtml#l00071">main_graph_alexnet()</a>, and <a class="el" href="utils_2_utils_8cpp_source.xhtml#l00069">arm_compute::utils::run_example()</a>.</p>
<div class="fragment"><div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;{</div>
<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;    <span class="keywordflow">return</span> <a class="code" href="namespacearm__compute_1_1utils.xhtml#a4c9395db2c8b8d0c336656a7b58fca3e">arm_compute::utils::run_example</a>(argc, argv, <a class="code" href="graph__alexnet_8cpp.xhtml#aa2e0960766de068c13ddff2ab22a8c35">main_graph_alexnet</a>);</div>
<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;}</div>
<div class="ttc" id="graph__alexnet_8cpp_xhtml_aa2e0960766de068c13ddff2ab22a8c35"><div class="ttname"><a href="graph__alexnet_8cpp.xhtml#aa2e0960766de068c13ddff2ab22a8c35">main_graph_alexnet</a></div><div class="ttdeci">void main_graph_alexnet(int argc, const char **argv)</div><div class="ttdoc">Example demonstrating how to implement AlexNet&#39;s network using the Compute Library&#39;s graph API...</div><div class="ttdef"><b>Definition:</b> <a href="graph__alexnet_8cpp_source.xhtml#l00071">graph_alexnet.cpp:71</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1utils_xhtml_a4c9395db2c8b8d0c336656a7b58fca3e"><div class="ttname"><a href="namespacearm__compute_1_1utils.xhtml#a4c9395db2c8b8d0c336656a7b58fca3e">arm_compute::utils::run_example</a></div><div class="ttdeci">int run_example(int argc, const char **argv, example &amp;func)</div><div class="ttdoc">Run an example and handle the potential exceptions it throws. </div><div class="ttdef"><b>Definition:</b> <a href="utils_2_utils_8cpp_source.xhtml#l00069">Utils.cpp:69</a></div></div>
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          <td class="memname">void main_graph_alexnet </td>
          <td>(</td>
          <td class="paramtype">int&#160;</td>
          <td class="paramname"><em>argc</em>, </td>
        </tr>
        <tr>
          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">const char **&#160;</td>
          <td class="paramname"><em>argv</em>&#160;</td>
        </tr>
        <tr>
          <td></td>
          <td>)</td>
          <td></td><td></td>
        </tr>
      </table>
</div><div class="memdoc">

<p>Example demonstrating how to implement AlexNet's network using the Compute Library's graph API. </p>
<dl class="params"><dt>Parameters</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">argc</td><td>Number of arguments </td></tr>
    <tr><td class="paramdir">[in]</td><td class="paramname">argv</td><td>Arguments ( [optional] Path to the weights folder, [optional] batches ) </td></tr>
  </table>
  </dd>
</dl>
<p>Path to the trainable data</p>
<p>Number of batches </p>

<p>Definition at line <a class="el" href="graph__alexnet_8cpp_source.xhtml#l00071">71</a> of file <a class="el" href="graph__alexnet_8cpp_source.xhtml">graph_alexnet.cpp</a>.</p>

<p>References <a class="el" href="namespacearm__compute.xhtml#ad4bb8dabdbf8ad75e34220cc666b59caa980fef040549733973683b1a868f96e5">arm_compute::CROSS_MAP</a>, <a class="el" href="_c_l_scheduler_8h_source.xhtml#l00083">CLScheduler::default_init()</a>, <a class="el" href="namespacearm__compute_1_1graph.xhtml#a9a92cf6a83b4d54786334cc37a7391a2a4c5d06b02c97731aaa976179c62dcf76">arm_compute::graph::DIRECT</a>, <a class="el" href="namespacearm__compute.xhtml#ab4e88c89b3b7ea1735996cc4def22d58a44ad4ef5a76e6aa6fb3e3fa079a54fda">arm_compute::F32</a>, <a class="el" href="classarm__compute_1_1_logger.xhtml#a6b2499aec6ae645d88670be75dcef769">Logger::get()</a>, <a class="el" href="classarm__compute_1_1_c_l_scheduler.xhtml#a60f9a6836b628a7171914c4afe43b4a7">CLScheduler::get()</a>, <a class="el" href="graph__alexnet_8cpp_source.xhtml#l00054">get_accessor()</a>, <a class="el" href="namespacearm__compute.xhtml#afb2e0528558bbb0131d2cb41a66c13d7a551b723eafd6a31d444fcb2f5920fbd3">arm_compute::INFO</a>, <a class="el" href="namespacearm__compute.xhtml#adf2ced65e536375a1c96425d9fced858a26a4b44a837bf97b972628509912b4a5">arm_compute::MAX</a>, <a class="el" href="namespacearm__compute_1_1graph.xhtml#a8d5e69e9a697c2007e241eb413c9833bacaf162e9233294cadf62d2a71a14ca09">arm_compute::graph::NEON</a>, <a class="el" href="namespacearm__compute_1_1graph.xhtml#a8d5e69e9a697c2007e241eb413c9833ba542f952490e2db695a1d544338a70cda">arm_compute::graph::OPENCL</a>, <a class="el" href="namespacearm__compute.xhtml#aa4f4d7a58287017588fc338965873f14">arm_compute::opencl_is_available()</a>, <a class="el" href="classarm__compute_1_1_activation_layer_info.xhtml#a56297e0f7b215eea46c818cb7528d9eaad346bb4679d29be241279f15d7795c1c">ActivationLayerInfo::RELU</a>, <a class="el" href="classarm__compute_1_1graph_1_1_graph.xhtml#a13a43e6d814de94978c515cb084873b1">Graph::run()</a>, and <a class="el" href="classarm__compute_1_1_logger.xhtml#a8ce097d129855b3ce7f3fcd6c30551ba">Logger::set_logger()</a>.</p>

<p>Referenced by <a class="el" href="graph__alexnet_8cpp_source.xhtml#l00181">main()</a>.</p>
<div class="fragment"><div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;{</div>
<div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;    std::string  data_path;   </div>
<div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> batches = 4; </div>
<div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;    <span class="comment">// Parse arguments</span></div>
<div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;    <span class="keywordflow">if</span>(argc &lt; 2)</div>
<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;    {</div>
<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;        <span class="comment">// Print help</span></div>
<div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;        std::cout &lt;&lt; <span class="stringliteral">&quot;Usage: &quot;</span> &lt;&lt; argv[0] &lt;&lt; <span class="stringliteral">&quot; [path_to_data] [batches]\n\n&quot;</span>;</div>
<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;        std::cout &lt;&lt; <span class="stringliteral">&quot;No data folder provided: using random values\n\n&quot;</span>;</div>
<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;    }</div>
<div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;    <span class="keywordflow">else</span> <span class="keywordflow">if</span>(argc == 2)</div>
<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;    {</div>
<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;        <span class="comment">//Do something with argv[1]</span></div>
<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;        data_path = argv[1];</div>
<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;        std::cout &lt;&lt; <span class="stringliteral">&quot;Usage: &quot;</span> &lt;&lt; argv[0] &lt;&lt; <span class="stringliteral">&quot; [path_to_data] [batches]\n\n&quot;</span>;</div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;        std::cout &lt;&lt; <span class="stringliteral">&quot;No number of batches where specified, thus will use the default : &quot;</span> &lt;&lt; batches &lt;&lt; <span class="stringliteral">&quot;\n\n&quot;</span>;</div>
<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;    }</div>
<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;    <span class="keywordflow">else</span></div>
<div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;    {</div>
<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;        <span class="comment">//Do something with argv[1] and argv[2]</span></div>
<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;        data_path = argv[1];</div>
<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;        batches   = std::strtol(argv[2], <span class="keyword">nullptr</span>, 0);</div>
<div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;    }</div>
<div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;</div>
<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;    <span class="comment">// Check if OpenCL is available and initialize the scheduler</span></div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;    <a class="code" href="namespacearm__compute_1_1graph.xhtml#a8d5e69e9a697c2007e241eb413c9833b">TargetHint</a> hint = TargetHint::NEON;</div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;    <span class="keywordflow">if</span>(<a class="code" href="namespacearm__compute.xhtml#aa4f4d7a58287017588fc338965873f14">arm_compute::opencl_is_available</a>())</div>
<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;    {</div>
<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;        <a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a60f9a6836b628a7171914c4afe43b4a7">arm_compute::CLScheduler::get</a>().<a class="code" href="classarm__compute_1_1_c_l_scheduler.xhtml#a46ecf9ef0fe80ba2ed35acfc29856b7d">default_init</a>();</div>
<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;        hint = TargetHint::OPENCL;</div>
<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;    }</div>
<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;</div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;    <a class="code" href="classarm__compute_1_1graph_1_1_graph.xhtml">Graph</a> graph;</div>
<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;    <a class="code" href="classarm__compute_1_1_logger.xhtml#a6b2499aec6ae645d88670be75dcef769">arm_compute::Logger::get</a>().<a class="code" href="classarm__compute_1_1_logger.xhtml#a8ce097d129855b3ce7f3fcd6c30551ba">set_logger</a>(std::cout, <a class="code" href="namespacearm__compute.xhtml#afb2e0528558bbb0131d2cb41a66c13d7a551b723eafd6a31d444fcb2f5920fbd3">arm_compute::LoggerVerbosity::INFO</a>);</div>
<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;</div>
<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;    graph &lt;&lt; hint</div>
<div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;          &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1_tensor.xhtml">Tensor</a>(<a class="code" href="classarm__compute_1_1_tensor_info.xhtml">TensorInfo</a>(<a class="code" href="classarm__compute_1_1_tensor_shape.xhtml">TensorShape</a>(227U, 227U, 3U, batches), 1, DataType::F32), <a class="code" href="classarm__compute_1_1graph__utils_1_1_dummy_accessor.xhtml">DummyAccessor</a>())</div>
<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;          <span class="comment">// Layer 1</span></div>
<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;          &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div>
<div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;              11U, 11U, 96U,</div>
<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;              <a class="code" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/alexnet_model/conv1_w.npy&quot;</span>),</div>
<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;              <a class="code" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/alexnet_model/conv1_b.npy&quot;</span>),</div>
<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;              <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(4, 4, 0, 0))</div>
<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;          &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(ActivationLayerInfo::ActivationFunction::RELU))</div>
<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;          &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1_normalization_layer.xhtml">NormalizationLayer</a>(<a class="code" href="classarm__compute_1_1_normalization_layer_info.xhtml">NormalizationLayerInfo</a>(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))</div>
<div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;          &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1_pooling_layer.xhtml">PoolingLayer</a>(<a class="code" href="classarm__compute_1_1_pooling_layer_info.xhtml">PoolingLayerInfo</a>(PoolingType::MAX, 3, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 0)))</div>
<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;          <span class="comment">// Layer 2</span></div>
<div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;          &lt;&lt; ConvolutionMethodHint::DIRECT</div>
<div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;          &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div>
<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;              5U, 5U, 256U,</div>
<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;              <a class="code" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/alexnet_model/conv2_w.npy&quot;</span>),</div>
<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;              <a class="code" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/alexnet_model/conv2_b.npy&quot;</span>),</div>
<div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;              <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 2, 2), 2)</div>
<div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;          &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(ActivationLayerInfo::ActivationFunction::RELU))</div>
<div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;          &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1_normalization_layer.xhtml">NormalizationLayer</a>(<a class="code" href="classarm__compute_1_1_normalization_layer_info.xhtml">NormalizationLayerInfo</a>(NormType::CROSS_MAP, 5, 0.0001f, 0.75f))</div>
<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;          &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1_pooling_layer.xhtml">PoolingLayer</a>(<a class="code" href="classarm__compute_1_1_pooling_layer_info.xhtml">PoolingLayerInfo</a>(PoolingType::MAX, 3, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 0)))</div>
<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;          <span class="comment">// Layer 3</span></div>
<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;          &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div>
<div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;              3U, 3U, 384U,</div>
<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;              <a class="code" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/alexnet_model/conv3_w.npy&quot;</span>),</div>
<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;              <a class="code" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/alexnet_model/conv3_b.npy&quot;</span>),</div>
<div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;              <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1))</div>
<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;          &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(ActivationLayerInfo::ActivationFunction::RELU))</div>
<div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;          <span class="comment">// Layer 4</span></div>
<div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;          &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div>
<div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;              3U, 3U, 384U,</div>
<div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;              <a class="code" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/alexnet_model/conv4_w.npy&quot;</span>),</div>
<div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;              <a class="code" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/alexnet_model/conv4_b.npy&quot;</span>),</div>
<div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;              <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1), 2)</div>
<div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;          &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(ActivationLayerInfo::ActivationFunction::RELU))</div>
<div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;          <span class="comment">// Layer 5</span></div>
<div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;          &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">ConvolutionLayer</a>(</div>
<div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;              3U, 3U, 256U,</div>
<div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;              <a class="code" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/alexnet_model/conv5_w.npy&quot;</span>),</div>
<div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;              <a class="code" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/alexnet_model/conv5_b.npy&quot;</span>),</div>
<div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;              <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(1, 1, 1, 1), 2)</div>
<div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;          &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(ActivationLayerInfo::ActivationFunction::RELU))</div>
<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;          &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1_pooling_layer.xhtml">PoolingLayer</a>(<a class="code" href="classarm__compute_1_1_pooling_layer_info.xhtml">PoolingLayerInfo</a>(PoolingType::MAX, 3, <a class="code" href="classarm__compute_1_1_pad_stride_info.xhtml">PadStrideInfo</a>(2, 2, 0, 0)))</div>
<div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;          <span class="comment">// Layer 6</span></div>
<div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;          &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1_fully_connected_layer.xhtml">FullyConnectedLayer</a>(</div>
<div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;              4096U,</div>
<div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;              <a class="code" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/alexnet_model/fc6_w.npy&quot;</span>),</div>
<div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;              <a class="code" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/alexnet_model/fc6_b.npy&quot;</span>))</div>
<div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;          &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(ActivationLayerInfo::ActivationFunction::RELU))</div>
<div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;          <span class="comment">// Layer 7</span></div>
<div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;          &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1_fully_connected_layer.xhtml">FullyConnectedLayer</a>(</div>
<div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;              4096U,</div>
<div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;              <a class="code" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/alexnet_model/fc7_w.npy&quot;</span>),</div>
<div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;              <a class="code" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/alexnet_model/fc7_b.npy&quot;</span>))</div>
<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;          &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1_activation_layer.xhtml">ActivationLayer</a>(<a class="code" href="classarm__compute_1_1_activation_layer_info.xhtml">ActivationLayerInfo</a>(ActivationLayerInfo::ActivationFunction::RELU))</div>
<div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;          <span class="comment">// Layer 8</span></div>
<div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;          &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1_fully_connected_layer.xhtml">FullyConnectedLayer</a>(</div>
<div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;              1000U,</div>
<div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;              <a class="code" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/alexnet_model/fc8_w.npy&quot;</span>),</div>
<div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;              <a class="code" href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a>(data_path, <span class="stringliteral">&quot;/cnn_data/alexnet_model/fc8_b.npy&quot;</span>))</div>
<div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;          <span class="comment">// Softmax</span></div>
<div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;          &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1_softmax_layer.xhtml">SoftmaxLayer</a>()</div>
<div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;          &lt;&lt; <a class="code" href="classarm__compute_1_1graph_1_1_tensor.xhtml">Tensor</a>(<a class="code" href="classarm__compute_1_1graph__utils_1_1_dummy_accessor.xhtml">DummyAccessor</a>());</div>
<div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;</div>
<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;    <span class="comment">// Run graph</span></div>
<div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;    graph.<a class="code" href="classarm__compute_1_1graph_1_1_graph.xhtml#a13a43e6d814de94978c515cb084873b1">run</a>();</div>
<div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;}</div>
<div class="ttc" id="classarm__compute_1_1_tensor_shape_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_shape.xhtml">arm_compute::TensorShape</a></div><div class="ttdoc">Shape of a tensor. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_shape_8h_source.xhtml#l00038">TensorShape.h:38</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1_fully_connected_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_fully_connected_layer.xhtml">arm_compute::graph::FullyConnectedLayer</a></div><div class="ttdoc">Fully connected layer node. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2nodes_2_fully_connected_layer_8h_source.xhtml#l00037">FullyConnectedLayer.h:37</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1_graph_xhtml_a13a43e6d814de94978c515cb084873b1"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_graph.xhtml#a13a43e6d814de94978c515cb084873b1">arm_compute::graph::Graph::run</a></div><div class="ttdeci">void run()</div><div class="ttdoc">Executes the graph. </div></div>
<div class="ttc" id="classarm__compute_1_1graph__utils_1_1_dummy_accessor_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph__utils_1_1_dummy_accessor.xhtml">arm_compute::graph_utils::DummyAccessor</a></div><div class="ttdoc">Dummy accessor class. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_utils_8h_source.xhtml#l00060">GraphUtils.h:60</a></div></div>
<div class="ttc" id="classarm__compute_1_1_normalization_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_normalization_layer_info.xhtml">arm_compute::NormalizationLayerInfo</a></div><div class="ttdoc">Normalization Layer Information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00561">Types.h:561</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_aa4f4d7a58287017588fc338965873f14"><div class="ttname"><a href="namespacearm__compute.xhtml#aa4f4d7a58287017588fc338965873f14">arm_compute::opencl_is_available</a></div><div class="ttdeci">bool opencl_is_available()</div></div>
<div class="ttc" id="classarm__compute_1_1_activation_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_activation_layer_info.xhtml">arm_compute::ActivationLayerInfo</a></div><div class="ttdoc">Activation Layer Information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00511">Types.h:511</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1_normalization_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_normalization_layer.xhtml">arm_compute::graph::NormalizationLayer</a></div><div class="ttdoc">Normalization layer node. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2nodes_2_normalization_layer_8h_source.xhtml#l00036">NormalizationLayer.h:36</a></div></div>
<div class="ttc" id="graph__alexnet_8cpp_xhtml_acbea98d13e0adbf27ecc036feeb610f0"><div class="ttname"><a href="graph__alexnet_8cpp.xhtml#acbea98d13e0adbf27ecc036feeb610f0">get_accessor</a></div><div class="ttdeci">std::unique_ptr&lt; ITensorAccessor &gt; get_accessor(const std::string &amp;path, const std::string &amp;data_file)</div><div class="ttdoc">Generates appropriate accessor according to the specified path. </div><div class="ttdef"><b>Definition:</b> <a href="graph__alexnet_8cpp_source.xhtml#l00054">graph_alexnet.cpp:54</a></div></div>
<div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_a60f9a6836b628a7171914c4afe43b4a7"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#a60f9a6836b628a7171914c4afe43b4a7">arm_compute::CLScheduler::get</a></div><div class="ttdeci">static CLScheduler &amp; get()</div><div class="ttdoc">Access the scheduler singleton. </div></div>
<div class="ttc" id="classarm__compute_1_1_pad_stride_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pad_stride_info.xhtml">arm_compute::PadStrideInfo</a></div><div class="ttdoc">Padding and stride information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00406">Types.h:406</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1_softmax_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_softmax_layer.xhtml">arm_compute::graph::SoftmaxLayer</a></div><div class="ttdoc">Softmax layer node. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2nodes_2_softmax_layer_8h_source.xhtml#l00036">SoftmaxLayer.h:36</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1_graph_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_graph.xhtml">arm_compute::graph::Graph</a></div><div class="ttdoc">Graph class. </div><div class="ttdef"><b>Definition:</b> <a href="_graph_8h_source.xhtml#l00041">Graph.h:41</a></div></div>
<div class="ttc" id="namespacearm__compute_xhtml_afb2e0528558bbb0131d2cb41a66c13d7a551b723eafd6a31d444fcb2f5920fbd3"><div class="ttname"><a href="namespacearm__compute.xhtml#afb2e0528558bbb0131d2cb41a66c13d7a551b723eafd6a31d444fcb2f5920fbd3">arm_compute::LoggerVerbosity::INFO</a></div><div class="ttdoc">Log info. </div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1_pooling_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_pooling_layer.xhtml">arm_compute::graph::PoolingLayer</a></div><div class="ttdoc">Pooling layer node. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2nodes_2_pooling_layer_8h_source.xhtml#l00037">PoolingLayer.h:37</a></div></div>
<div class="ttc" id="namespacearm__compute_1_1graph_xhtml_a8d5e69e9a697c2007e241eb413c9833b"><div class="ttname"><a href="namespacearm__compute_1_1graph.xhtml#a8d5e69e9a697c2007e241eb413c9833b">arm_compute::graph::TargetHint</a></div><div class="ttdeci">TargetHint</div><div class="ttdoc">&lt; Execution hint to the graph executor </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2_types_8h_source.xhtml#l00050">Types.h:50</a></div></div>
<div class="ttc" id="classarm__compute_1_1_tensor_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_tensor_info.xhtml">arm_compute::TensorInfo</a></div><div class="ttdoc">Store the tensor&#39;s metadata. </div><div class="ttdef"><b>Definition:</b> <a href="_tensor_info_8h_source.xhtml#l00042">TensorInfo.h:42</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1_activation_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_activation_layer.xhtml">arm_compute::graph::ActivationLayer</a></div><div class="ttdoc">Activation Layer node. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2nodes_2_activation_layer_8h_source.xhtml#l00037">ActivationLayer.h:37</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1_convolution_layer_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_convolution_layer.xhtml">arm_compute::graph::ConvolutionLayer</a></div><div class="ttdoc">Convolution layer node. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2graph_2nodes_2_convolution_layer_8h_source.xhtml#l00041">ConvolutionLayer.h:41</a></div></div>
<div class="ttc" id="classarm__compute_1_1_pooling_layer_info_xhtml"><div class="ttname"><a href="classarm__compute_1_1_pooling_layer_info.xhtml">arm_compute::PoolingLayerInfo</a></div><div class="ttdoc">Pooling Layer Information class. </div><div class="ttdef"><b>Definition:</b> <a href="arm__compute_2core_2_types_8h_source.xhtml#l00445">Types.h:445</a></div></div>
<div class="ttc" id="classarm__compute_1_1_logger_xhtml_a6b2499aec6ae645d88670be75dcef769"><div class="ttname"><a href="classarm__compute_1_1_logger.xhtml#a6b2499aec6ae645d88670be75dcef769">arm_compute::Logger::get</a></div><div class="ttdeci">static Logger &amp; get()</div></div>
<div class="ttc" id="classarm__compute_1_1_logger_xhtml_a8ce097d129855b3ce7f3fcd6c30551ba"><div class="ttname"><a href="classarm__compute_1_1_logger.xhtml#a8ce097d129855b3ce7f3fcd6c30551ba">arm_compute::Logger::set_logger</a></div><div class="ttdeci">void set_logger(std::ostream &amp;ostream, LoggerVerbosity verbosity)</div></div>
<div class="ttc" id="classarm__compute_1_1_c_l_scheduler_xhtml_a46ecf9ef0fe80ba2ed35acfc29856b7d"><div class="ttname"><a href="classarm__compute_1_1_c_l_scheduler.xhtml#a46ecf9ef0fe80ba2ed35acfc29856b7d">arm_compute::CLScheduler::default_init</a></div><div class="ttdeci">void default_init(ICLTuner *cl_tuner=nullptr)</div><div class="ttdoc">Initialises the context and command queue used by the scheduler to default values and sets a default ...</div><div class="ttdef"><b>Definition:</b> <a href="_c_l_scheduler_8h_source.xhtml#l00083">CLScheduler.h:83</a></div></div>
<div class="ttc" id="classarm__compute_1_1graph_1_1_tensor_xhtml"><div class="ttname"><a href="classarm__compute_1_1graph_1_1_tensor.xhtml">arm_compute::graph::Tensor</a></div><div class="ttdoc">Tensor class. </div><div class="ttdef"><b>Definition:</b> <a href="graph_2_tensor_8h_source.xhtml#l00038">Tensor.h:38</a></div></div>
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